A nonparametric approach to Gaussian FPCA was introduced by [1]}
and adapted by [2]} for the non-Gaussian case.
[3]} extended this method to further handle
multilevel binary data with potentially rare events.
[4]} used a similar approach to model point processes
with a spatio-temporal correlation structure.
[5]} showed that the marginal mean estimates proposed by
[2]} can introduce bias in non-Gaussian data settings and tackled this issue by plugging the eigenfunction estimates
into a generalized additive mixed model to achieve an estimation
of the mean structure conditional on FPC scores represented as random effects.
